Diets enriched with palm olein, cocoa butter, and extra virgin olive oil exhibited similar lipid response: a randomized controlled study in young healthy adults.

Vegetable oils having unsaturated fatty acids in the sn-2 position of triglyceride (TG) backbone might not raise serum cholesterol levels. We investigated the chronic effects of diets enriched with palm olein (IV64) (PO), cocoa butter (CB), or extra virgin olive oil (EVOO) with oleic acid primarily at the sn-2 position (66%, 75%, 87% sn-2 oleic acid, respectively) of the TG molecule in 40 healthy volunteers participated in this randomized, controlled, single-blinded, crossover trial. Following a 2-week run-in period, the subjects were given standardization meals (breakfast, lunch, and dinner) cooked with palm olein (IV72). Subjects were randomized to 1 of the 3 intervention groups; receiving baked products (brownies for breakfast and cookies for teatime) prepared with respective test fats accompanied with standardized low-fat meals for breakfast, lunch, and dinner prepared with palm olein (IV72) for all groups for 4 weeks in a crossover manner with 2-week washout period (given standardization meals). Anthropometric measurements, blood samples, and dietary intakes were measured before run-in and pre- and post-intervention. No significant difference was observed on the primary outcome of the study total: high-density lipoprotein cholesterol. All 3 test fats were found to exhibit similar lipid responses (total cholesterol, TG, lipoprotein (a), apolipoprotein-A1, apolipoprotein-B/A-1). Statistical difference was found on low-density lipoprotein cholesterol (CB>EVOO by 0.3 mmol/L, P = .003), high-density lipoprotein cholesterol (PO>CB by 0.04 mmol/L, P = .02) and apolipoprotein-B (EVOO

Interesterified Palm Olein (IEPalm) and Interesterified Stearic Acid-Rich Fat Blend (IEStear) Have No Adverse Effects on Insulin Resistance: A Randomized Control Trial.

Chemically-interesterified (CIE) fats are trans-fat free and are increasingly being used as an alternative to hydrogenated oils for food manufacturing industries to optimize their products' characteristics and nutrient compositions. The metabolic effects of CIE fats on insulin activity, lipids, and adiposity in humans are not well established. We investigated the effects of CIE fats rich in palmitic (C16:0, IEPalm) and stearic (C18:0, IEStear) acids on insulin resistance, serum lipids, apolipoprotein concentrations, and adiposity, using C16:0-rich natural palm olein (NatPO) as the control. We designed a parallel, double-blind clinical trial. Three test fats were used to prepare daily snacks for consumption with a standard background diet over a period of 8 weeks by three groups of a total of 85 healthy, overweight adult volunteers. We measured the outcome variables at weeks 0, 6, and at the endpoint of 8. After 8 weeks, there was no significant difference in surrogate biomarkers of insulin resistance in any of the IE fat diets (IEPalm and IEStear) compared to the NatPO diet. The change in serum triacylglycerol concentrations was significantly lower with the IEStear diet, and the changes in serum leptin and body fat percentages were significantly lower in the NatPO-diet compared to the IEPalm diet. We conclude that diets containing C16:0 and C18:0-rich CIE fats do not affect markers of insulin resistance compared to a natural C16:0-rich fat (NatPO) diet. Higher amounts of saturated fatty acids (SFAs) and longer chain SFAs situated at the sn-1,3 position of the triacylglycerol (TAG) backbones resulted in less weight gain and lower changes in body fat percentage and leptin concentration to those observed in NatPO and IEStear.

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data.

In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing.

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Image analysis using hahn moments.

This paper shows how Hahn moments provide a unified understanding of the recently introduced Chebyshev and Krawtchouk moments. The two latter moments can be obtained as particular cases of Hahn moments with the appropriate parameter settings, and this fact implies that Hahn moments encompass all their properties. The aim of this paper is twofold: 1) To show how Hahn moments, as a generalization of Chebyshev and Krawtchouk moments, can be used for global and local feature extraction, and 2) to show how Hahn moments can be incorporated into the framework of normalized convolution to analyze local structures of irregularly sampled signals.

Image analysis by Krawtchouk moments.

In this paper, a new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials. The orthogonality of the proposed moments ensures minimal information redundancy. No numerical approximation is involved in deriving the moments, since the weighted Krawtchouk polynomials are discrete. These properties make the Krawtchouk moments well suited as pattern features in the analysis of two-dimensional images. It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features. The computational aspects of the moments using the recursive and symmetry properties are discussed. The theoretical framework is validated by an experiment on image reconstruction using Krawtchouk moments and the results are compared to that of Zernike, pseudo-Zernike, Legendre, and Tchebyscheff moments. Krawtchouk moment invariants are constructed using a linear combination of geometric moment invariants; an object recognition experiment shows Krawtchouk moment invariants perform significantly better than Hu's moment invariants in both noise-free and noisy conditions.